| 10 Mar 2026
GeoSIRR 1.0: Conversational Geological Cross-Section Modeling Using Large Language Models
Abstract. Geological cross-sections are a fundamental tool for subsurface interpretation, yet their construction remains a labor-intensive and largely manual process that relies on expert judgment and structured numerical inputs. While recent advances in artificial intelligence have enhanced specific geoscientific workflows, no existing method enables the direct generation and iterative refinement of geological cross-sections from unstructured natural language descriptions. In this paper, we present GeoSIRR 1.0 (Geological Section Interpretation, Reconstruction & Refinement), a novel modeling framework that leverages large language models (LLMs) to translate free-form geological narratives into structured, coordinate-based cross-section geometries. GeoSIRR introduces a domain-specific language (DSL) for representing geological bodies as topologically consistent polygons and integrates automated geometric and geological validation to ensure continuity, stratigraphic consistency, and structural plausibility. The framework supports both initial model generation and conversational refinement, allowing users to iteratively modify cross-sections using natural language commands while preserving existing geometry. We demonstrate the capabilities of GeoSIRR through multiple geological scenarios, including faulted sedimentary systems, intrusive bodies, and progradational deltaic sequences, and assess repeatability across multiple generation runs. Results show that GeoSIRR consistently produces geologically plausible cross-sections and effectively incorporates conceptual refinements with reduced generation time compared to initial model construction. By directly linking qualitative geological reasoning with quantitative geometric modeling, GeoSIRR provides a self-contained, dialogue-driven approach to cross-section construction that complements existing modeling tools and offers new opportunities for education, exploratory analysis, and rapid scenario development in subsurface geoscience.
Anikiev et al. present GeoSIRR 1.0, a software tool that aims to construct geological cross-sections using Large Language Models (LLMs). Specifically, the authors developed a routine in which qualitative geological descriptions provided as prompts can be transformed into geological cross-sections via LLMs. This method is intended to replace labor-intensive manual cross-section reconstructions.
The code associated with GeoSIRR 1.0 is publicly available through GitHub and Zenodo repositories and the software can be installed easily via the command line. All examples discussed in the manuscript are, in principle, accessible. However, GeoSIRR 1.0 in its current form depends on GPT language models, and an OpenAI API key is required to run the cross-section reconstructions. When attempting to run the provided examples, I received error messages indicating that my API quota had been exceeded and that additional credits would need to be purchased to proceed. This dependency renders GeoSIRR 1.0 effectively inaccessible for reviewing or users without an active paid API subscription, meaning the results cannot be freely tested or reproduced.
Furthermore, the manuscript presents only relatively simple tectonic geometries as examples and does not thoroughly demonstrate how the method performs on more complex geometries or tectonic relationships. The authors indicate that increased complexity leads to a significant increase in computation time, yet no further testing is provided for geologically meaningful complex scenarios. Moreover, the limitations related to GeoSIRR 1.0, which are noted in the manuscript (no uncertainty quantification, unclear generation performance and repeatability), are serious and the model should not be considered without these issues being properly assessed.
For the reasons outlined above, I do not consider the manuscript to demonstrate a sufficiently innovative or significant scientific contribution while its results remain non-reproducible in its current form. Therefore, I recommend against acceptance.